Abstract:
In this paper we propose a framework based on Deep Reinforcement Learning to proactively and autonomously take under control links loads in Segment Routing (SR) networks....Show MoreMetadata
Abstract:
In this paper we propose a framework based on Deep Reinforcement Learning to proactively and autonomously take under control links loads in Segment Routing (SR) networks. The main idea is to monitor local link loads and, in case of anomalous situation, to execute local routing changes at milliseconds timescale. The solution proposed is based on a Multi Agent Reinforcement Learning (MARL) approach: a subset of nodes is equipped with a local agent, powered by a Deep Q-Network (DQN) algorithm, referred to as SRv6 rerouting for Local In-network Link Load Control (SR-LILLC). The main feature of SR-LILLC is to train the agents in a collaborative way, by defining a “shared” reward function, while working in an independent way during the operating phase. Moreover, the re-routing operation is performed in a transparent way for other network devices, without involving the centralized control plane, by exploiting the source routing feature of the SR. The performance evaluation conducted over real data sets shows that SR-LILLC is able to reduce the load on agents links without increasing the maximum link utilization of the network; moreover, the overall network performance are improved in terms of end-to-end delays.
Published in: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Date of Conference: 20-20 May 2023
Date Added to IEEE Xplore: 29 August 2023
ISBN Information: